Cerebrovascular amyloid Angiopathy in bioengineered vessels is reduced by high-density lipoprotein particles enriched in Apolipoprotein E
Bibliographic record
Abstract
BACKGROUND: Several lines of evidence suggest that high-density lipoprotein (HDL) reduces Alzheimer's disease (AD) risk by decreasing vascular beta-amyloid (Aβ) deposition and inflammation, however, the mechanisms by which HDL improve cerebrovascular functions relevant to AD remain poorly understood. METHODS: Here we use a human bioengineered model of cerebral amyloid angiopathy (CAA) to define several mechanisms by which HDL reduces Aβ deposition within the vasculature and attenuates endothelial inflammation as measured by monocyte binding. RESULTS: We demonstrate that HDL reduces vascular Aβ accumulation independently of its principal binding protein, scavenger receptor (SR)-BI, in contrast to the SR-BI-dependent mechanism by which HDL prevents Aβ-induced vascular inflammation. We describe multiple novel mechanisms by which HDL acts to reduce CAA, namely: i) altering Aβ binding to collagen-I, ii) forming a complex with Aβ that maintains its solubility, iii) lowering collagen-I protein levels produced by smooth-muscle cells (SMC), and iv) attenuating Aβ uptake into SMC that associates with reduced low density lipoprotein related protein 1 (LRP1) levels. Furthermore, we show that HDL particles enriched in apolipoprotein (apo)E appear to be the major drivers of these effects, providing new insights into the peripheral role of apoE in AD, in particular, the fraction of HDL that contains apoE. CONCLUSION: The findings in this study identify new mechanisms by which circulating HDL, particularly HDL particles enriched in apoE, may provide vascular resilience to Aβ and shed new light on a potential role of peripherally-acting apoE in AD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".